Pinned Repositories
Block-wise-Scrambled-Image-Recognition
Code for Adaptation Network introduced in "Block-wise Scrambled Image Recognition Using Adaptation Network" paper (AAAI WS 2020)
faster_rcnn_pytorch
Faster RCNN with PyTorch
gcce2022_instancewise_center_loss
psivt23_scramblemix
SPG_EI2020
Code of experiments in "Scrambling Parameter Generation to Improve Perceptual Information Hiding" paper (EI 2020)
MADONOKOUKI's Repositories
MADONOKOUKI/aaai-template
latex template for various conferences
MADONOKOUKI/AdaBound
An optimizer that trains as fast as Adam and as good as SGD.
MADONOKOUKI/AdaBound-Tensorflow
Simple Tensorflow implementation of "Adaptive Gradient Methods with Dynamic Bound of Learning Rate" (ICLR 2019)
MADONOKOUKI/BLeSS
Code for paper - D. Temel and G. AlRegib, “BLeSS: Bio-inspired Low-level Spatiochromatic Similarity Assisted Image Quality Assessment “, the IEEE International Conference on Multimedia and Expo , Seattle, USA, Jul. 11-15, 2016.
MADONOKOUKI/DNI
CVPR19 - Deep Network Interpolation for Continuous Imagery Effect Transition
MADONOKOUKI/Humpback-Whale-Identification-1st-
https://www.kaggle.com/c/humpback-whale-identification
MADONOKOUKI/Imagenet32_Scripts
Scripts for Imagenet 32 dataset
MADONOKOUKI/kervolution-pytorch
Pytorch Implementation of the Kernel Convolution AKA Kervolution Layer from Kervolutional Neural Networks (https://arxiv.org/pdf/1904.03955.pdf)
MADONOKOUKI/Learning-Gumbel-Sinkhorn-Permutations-w-Pytorch
LEARNING LATENT PERMUTATIONS WITH GUMBEL-SINKHORN NETWORKS IMPLEMENTATION WITH PYTORCH
MADONOKOUKI/mixup-cifar10
mixup: Beyond Empirical Risk Minimization
MADONOKOUKI/mnist_autoencoder
MADONOKOUKI/myGradientLib
simple C++ auto-differentiation library
MADONOKOUKI/optuna
A hyperparameter optimization framework
MADONOKOUKI/perm-optim
[ICLR 2019] Learning Representations of Sets through Optimized Permutations
MADONOKOUKI/PerSIM
Code for paper - D. Temel and G. AlRegib, "PerSIM: Multi-resolution image quality assessment in the perceptually uniform color domain," 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, 2015, pp. 1682-1686.
MADONOKOUKI/pybrisque
A python implementation of BRISQUE Image Quality Assessment
MADONOKOUKI/pytorch-auto-augment
PyTorch implementation of AutoAugment.
MADONOKOUKI/pytorch-center-loss
Pytorch implementation of Center Loss
MADONOKOUKI/pytorch-cifar
95.16% on CIFAR10 with PyTorch
MADONOKOUKI/pytorch-fid
A Port of Fréchet Inception Distance (FID score) to PyTorch
MADONOKOUKI/pytorch-frechet-inception-distance
A Pytorch Implementation of the Fréchet Inception Distance (FID)
MADONOKOUKI/PyTorch-GAN
PyTorch implementations of Generative Adversarial Networks.
MADONOKOUKI/pytorch-msssim
PyTorch differentiable Multi-Scale Structural Similarity (MS-SSIM) loss
MADONOKOUKI/pytorch-ricap
PyTorch implementation of RICAP (Random Image Cropping And Patching)
MADONOKOUKI/pytorch.sngan_projection
An unofficial PyTorch implementation of SNGAN (ICLR 2018) and cGANs with projection discriminator (ICLR 2018).
MADONOKOUKI/set_transformer
Pytorch implementation of set transformer
MADONOKOUKI/sparsemax-pytorch
Implementation of Sparsemax activation in Pytorch
MADONOKOUKI/tensorflow
An Open Source Machine Learning Framework for Everyone
MADONOKOUKI/UNIQUE-Unsupervised-Image-Quality-Estimation
This is a demonstration of the algorithm described in the paper "UNIQUE: Unsupervised Image Quality Estimation". Given an original and a distorted image, UNIQUE gives out a score that approximates the perceptual quality of a distorted image.
MADONOKOUKI/WideResNet-pytorch
Wide Residual Networks (WideResNets) in PyTorch